A new, more effective method for targeting patients that will respond to treatment could result in better outcomes and cost savings
Rheumatoid arthritis (RA) is a chronic and progressively debilitating condition, with a detrimental impact on society through disability, treatment costs and lost work time.
The introduction of biological (antibody) therapies which inhibit tumour necrosis factor alpha activity (TNFi) has transformed the management of RA, yet in around 35% of RA patients these drugs fail to adequately control the disease. In 2017 just two of these drugs, Adalimumab and Etanercept accounted for combined worldwide sales of $26Bn.
Although there are several TNFi therapies available, treatment selection is experiential, based largely on trial-and-error and many patients endure significant time on ineffective medicines detrimental to quality of life, risking long term joint damage, increased exposure to adverse events and healthcare systems incur significant wasted and additional treatment costs.
This technology will prospectively identify the significant number of patients who will not respond to TNFi therapy, enabling them to obtain efficacious treatments sooner and saving costs to the healthcare system.
This invention has arisen from the unexpected finding that peptide-based immunoprofiling of RA patient serum antibodies can be used to distinguish between responders and non-responders, even before treatment is initiated.
The team’s approach has competitive advantage in that immunoassay methods are well established, are robust and do not require highly specialised equipment. Recording a complex immunoprofile, rather than a single measurement, greatly enhances the scope for optimisation of the method and application of machine learning-based methodologies to increase predictive capability.
The team’s current priorities are to extend the number of serum samples tested and thus increase statistical validity, to define and describe the key parameters associated with a clinical diagnostic test and to carry out health economic analysis to drive a value-based pricing strategy.
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